Computer Vision using Azure AI and ML

We will be giving away Raspberry Pi IoT Kits during our session. ;)
Security is an important aspect of our lives and we use various means of security every day. In order for us to secure items in our lives, we have to have a trust factor. This applies to our bank accounts, our passwords, and even vehicles that we utilize every day. Sometimes, security can be complex -- in the case of bank account being accessed over the internet, or a rolling security codes in your car's remote start module, or RFID and barcodes in your driver license or passport.
At the end of the day, trust is what allows someone or a network system to allows for access. When you go to the bank, you have to provide a proof of who you are in a form of ID to even have a banker access your account. Now, let's think of something simple. Let's go back in time. Imagine that you are a newborn baby. The first few things you learn to trust is the sound of your mother. Your eyes are nowhere near 20-20 vision and the only thing you are you know is that you are potentially hungry. An infant mind has to associate the sound of your parent by a learned process. As you get older, you can now see a little better. As your senses grow, you learn to use them to authenticate your trust factors. Within months, you learn to recognize faces. You learn to associate familiar faces as good (trustworthy) people while unknowns as (potentially) bad non-trustworthy people in your short life thus far.
Computers can be trained to think and act just like humans as well. Using technologies such as Microsoft's Cognitive Services, we use to duplicate the learning processes that we learned as infants. We don't have to use 256k secured keys to protect the contents of our digital lives. We can use our faces, voices, fingerprints and more to allow for physical computers to issue trust using biological traces that are unique to us humans. The best part is, just like infants, the computers can be taught to trust the good identifiable features from bad ones by reinforced training. Let's see how that is accomplished.